Courtney Hebert1, Jennifer Flaherty2, Justin Smyer3, Jing Ding4, Julie E Mangino5. 1. Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH; Division of Infectious Diseases, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH. Electronic address: Courtney.Hebert@osumc.edu. 2. Department of Clinical Epidemiology, The Ohio State University Wexner Medical Center, Columbus, OH. 3. Department of Clinical Epidemiology, James Cancer Hospital and Solove Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH. 4. Information Warehouse, The Ohio State University Wexner Medical Center, Columbus, OH. 5. Division of Infectious Diseases, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH; Department of Clinical Epidemiology, The Ohio State University Wexner Medical Center, Columbus, OH.
Abstract
BACKGROUND: Surveillance is an important tool for infection control; however, this task can often be time-consuming and take away from infection prevention activities. With the increasing availability of comprehensive electronic health records, there is an opportunity to automate these surveillance activities. The objective of this article is to describe the implementation of an electronic algorithm for ventilator-associated events (VAEs) at a large academic medical center METHODS: This article reports on a 6-month manual validation of a dashboard for VAEs. We developed a computerized algorithm for automatically detecting VAEs and compared the output of this algorithm to the traditional, manual method of VAE surveillance. RESULTS: Manual surveillance by the infection preventionists identified 13 possible and 11 probable ventilator-associated pneumonias (VAPs), and the VAE dashboard identified 16 possible and 13 probable VAPs. The dashboard had 100% sensitivity and 100% accuracy when compared with manual surveillance for possible and probable VAP. We report on the successfully implemented VAE dashboard. Workflow of the infection preventionists was simplified after implementation of the dashboard with subjective time-savings reported. CONCLUSIONS: Implementing a computerized dashboard for VAE surveillance at a medical center with a comprehensive electronic health record is feasible; however, this required significant initial and ongoing work on the part of data analysts and infection preventionists.
BACKGROUND: Surveillance is an important tool for infection control; however, this task can often be time-consuming and take away from infection prevention activities. With the increasing availability of comprehensive electronic health records, there is an opportunity to automate these surveillance activities. The objective of this article is to describe the implementation of an electronic algorithm for ventilator-associated events (VAEs) at a large academic medical center METHODS: This article reports on a 6-month manual validation of a dashboard for VAEs. We developed a computerized algorithm for automatically detecting VAEs and compared the output of this algorithm to the traditional, manual method of VAE surveillance. RESULTS: Manual surveillance by the infection preventionists identified 13 possible and 11 probable ventilator-associated pneumonias (VAPs), and the VAE dashboard identified 16 possible and 13 probable VAPs. The dashboard had 100% sensitivity and 100% accuracy when compared with manual surveillance for possible and probable VAP. We report on the successfully implemented VAE dashboard. Workflow of the infection preventionists was simplified after implementation of the dashboard with subjective time-savings reported. CONCLUSIONS: Implementing a computerized dashboard for VAE surveillance at a medical center with a comprehensive electronic health record is feasible; however, this required significant initial and ongoing work on the part of data analysts and infection preventionists.
Authors: Oliver Wolffers; Martin Faltys; Janos Thomann; Stephan M Jakob; Jonas Marschall; Tobias M Merz; Rami Sommerstein Journal: Sci Rep Date: 2021-11-15 Impact factor: 4.379